Robust remaining useful life estimation based on adaptive system representation using jacobian feature adaptation

Abstract

Systems and methods for estimating Remaining Useful Life (RUL) of equipment based on adaptive system representation, for example, using Jacobian Feature Adaptation are presented herein. The systems and methods presented herein demonstrate a workflow to utilize an adaptation technique (offline as well as online) in order to have a more accurate and robust estimation of RUL of equipment for data-driven and hybrid models without needing to build multiple fault propagation models or having to retrain the model from scratch after collecting a sufficient amount of failure data, and demonstrate the application of the online and offline adaptation algorithms on applications relevant to the oil and gas industry, such as membranes, compressors, and so forth.

What is claimed is: 
     
         1 . A method, comprising:
 initially training, via an analysis and control system, a model of a physical system, wherein the model of the physical system comprises a data-driven model or a hybrid model that comprises a combination of a physics-based definition of the physical system and data collected relating to the physical system;   detecting, via the analysis and control system, deviations of one or more outputs of the model of the physical system relative to data collected by one or more sensors associated with the physical system during operation of the physical system;   determining, via the analysis and control system, that degradation in an ability of the model of the physical system to estimate performance of the physical system has occurred based at least in part on the detected deviations;   utilizing, via the analysis and control system, transfer learning or adaptation techniques of the model of the physical system to adapt the model of the physical system; and   estimating, via the analysis and control system, a Remaining Useful Life (RUL) of the physical system based on the adapted model of the physical system.   
     
     
         2 . The method of  claim 1 , comprising utilizing, via the analysis and control system, Jacobian Feature Regression (JFR) of the model of the physical system to adapt the model of the physical system. 
     
     
         3 . The method of  claim 1 , comprising automatically controlling, via the analysis and control system, one or more operational parameters of the physical system based at least in part on the estimated RUL of the physical system. 
     
     
         4 . The method of  claim 1 , comprising determining, via the analysis and control system, that the degradation in the ability of the model of the physical system to estimate the performance of the physical system has occurred, in response to detecting that the deviations of the one or more outputs of the model of the physical system relative to the data collected by the one or more sensors associated with the physical system are greater than predetermined thresholds. 
     
     
         5 . The method of  claim 1 , comprising continuously monitoring, via the analysis and control system, the one or more sensors to automatically detect the deviations of the one or more outputs of the model of the physical system relative to the data collected by the one or more sensors associated with the physical system during operation of the physical system. 
     
     
         6 . The method of  claim 1 , comprising utilizing, via the analysis and control system, the transfer learning or adaptation techniques on a state-space formulation of the model of the physical system to adapt the model of the physical system. 
     
     
         7 . The method of  claim 1 , wherein the model of the physical system comprises a recurrent neural network (RNN). 
     
     
         8 . An analysis and control system, comprising:
 one or more processors configured to execute processor-executable instructions stored in memory of the analysis and control system, wherein the processor-executable instructions, when executed by the one or more processors, cause the analysis and control system to:
 initially train a model of a physical system, wherein the model of the physical system comprises a data-driven model or a hybrid model that comprises a combination of a physics-based definition of the physical system and data collected relating to the physical system; 
 detect deviations of one or more outputs of the model of the physical system relative to data collected by one or more sensors associated with the physical system during operation of the physical system; 
 determine that degradation in an ability of the model of the physical system to estimate performance of the physical system has occurred based at least in part on the detected deviations; 
 utilize transfer learning or adaptation techniques of the model of the physical system to adapt the model of the physical system; and 
 estimate a Remaining Useful Life (RUL) of the physical system based on the adapted model of the physical system. 
   
     
     
         9 . The analysis and control system of  claim 8 , wherein the processor-executable instructions, when executed by the one or more processors, cause the analysis and control system to utilize Jacobian Feature Regression (JFR) of the model of the physical system to adapt the model of the physical system. 
     
     
         10 . The analysis and control system of  claim 8 , wherein the processor-executable instructions, when executed by the one or more processors, cause the analysis and control system to automatically control one or more operational parameters of the physical system based at least in part on the estimated RUL of the physical system. 
     
     
         11 . The analysis and control system of  claim 8 , wherein the processor-executable instructions, when executed by the one or more processors, cause the analysis and control system to determine that the degradation in the ability of the model of the physical system to estimate the performance of the physical system has occurred, in response to detecting that the deviations of the one or more outputs of the model of the physical system relative to the data collected by the one or more sensors associated with the physical system are greater than predetermined thresholds. 
     
     
         12 . The analysis and control system of  claim 8 , wherein the processor-executable instructions, when executed by the one or more processors, cause the analysis and control system to continuously monitor the one or more sensors to automatically detect the deviations of the one or more outputs of the model of the physical system relative to the data collected by the one or more sensors associated with the physical system during operation of the physical system. 
     
     
         13 . The analysis and control system of  claim 8 , wherein the processor-executable instructions, when executed by the one or more processors, cause the analysis and control system to utilize the transfer learning or adaptation techniques on a state-space formulation of the model of the physical system to adapt the model of the physical system. 
     
     
         14 . The analysis and control system of  claim 8 , wherein the model of the physical system comprises a recurrent neural network (RNN). 
     
     
         15 . A non-transitory computer readable medium, comprising:
 processor-executable instructions, which when executed by one or more processors of an analysis and control system, cause the analysis and control system to:
 initially train a model of a physical system, wherein the model of the physical system comprises a data-driven model or a hybrid model that comprises a combination of a physics-based definition of the physical system and data collected relating to the physical system; 
 detect deviations of one or more outputs of the model of the physical system relative to data collected by one or more sensors associated with the physical system during operation of the physical system; 
 determine that degradation in an ability of the model of the physical system to estimate performance of the physical system has occurred based at least in part on the detected deviations; 
 utilize transfer learning or adaptation techniques of the model of the physical system to adapt the model of the physical system; and 
 estimate a Remaining Useful Life (RUL) of the physical system based on the adapted model of the physical system. 
   
     
     
         16 . The non-transitory computer readable medium of  claim 15 , wherein the processor-executable instructions, when executed by the one or more processors of the analysis and control system, cause the analysis and control system to utilize Jacobian Feature Regression (JFR) of the model of the physical system to adapt the model of the physical system. 
     
     
         17 . The non-transitory computer readable medium of  claim 15 , wherein the processor-executable instructions, when executed by the one or more processors of the analysis and control system, cause the analysis and control system to automatically control one or more operational parameters of the physical system based at least in part on the estimated RUL of the physical system. 
     
     
         18 . The non-transitory computer readable medium of  claim 15 , wherein the processor-executable instructions, when executed by the one or more processors of the analysis and control system, cause the analysis and control system to determine that the degradation in the ability of the model of the physical system to estimate the performance of the physical system has occurred, in response to detecting that the deviations of the one or more outputs of the model of the physical system relative to the data collected by the one or more sensors associated with the physical system are greater than predetermined thresholds. 
     
     
         19 . The non-transitory computer readable medium of  claim 15 , wherein the processor-executable instructions, when executed by the one or more processors of the analysis and control system, cause the analysis and control system to continuously monitor the one or more sensors to automatically detect the deviations of the one or more outputs of the model of the physical system relative to the data collected by the one or more sensors associated with the physical system during operation of the physical system. 
     
     
         20 . The non-transitory computer readable medium of  claim 15 , wherein the processor-executable instructions, when executed by the one or more processors of the analysis and control system, cause the analysis and control system to utilize the transfer learning or adaptation techniques on a state-space formulation of the model of the physical system to adapt the model of the physical system.